Risk Factor Analysis (II) Presented by James M. Scanlan, Ph.D. U. of Washington Health Sciences Center Dept. of Psychiatry & Behavioral Sciences and Xiaowei Song, Ph.D. Dalhousie University Department of Medicine

Goals Examine combinations of major risk factors in regressions predicting 3MS in last year of study Include combined CHD/health risk index Examine results for possible interactions Controlling for baseline 3MS scores, which variables are most predictive of final 3MS ( thus predictive of decline rather than lower but constant 3MS values)

Index using 5 Risk Factor Items

Examined all possible interactions between: Age Race Apoe4 Education Main effects evident NO Interactions

3MS (at year 11) Age groups (65-69, 70-74, 75-79, 80+) Solid: high education 3MS (at year 11) Dash: low education Blue=Caucasians, Red=African Americans Age groups (65-69, 70-74, 75-79, 80+)

Solid: Apoe- 3MS (at year 11) Dash: Apoe+ Blue=Caucasians, Red=African Americans Age groups (65-69, 70-74, 75-79, 80+)

Solid: low risk index 3MS (at year 11) Dash: high risk index Blue=Caucasians, Red=African Americans Age groups (65-69, 70-74, 75-79, 80+) Two possible reasons about why the risk factor index did not look very good in this plot: 1. Only 5 risk items are available. 2. The precision is lowered down by dichotomization.

Summary Age, education, race, Apoe4 and CHD risk index influence 3MS results in regression Age, education and ethnicity first three variables in regression uncontrolled for initial 3MS value Main effects, but no interactions evident When initial 3MS score is controlled, age, CHD risk and education remain significant predictors of final 3MS score, but ethnicity is not. Results suggest that black ethnicity may influence initial starting 3MS score, but not necessarily the course of decline or change in 3MS scores.